Biology subjects

Vahidi, P.

Publications and source records attributed to Vahidi, P..

5 recordsLinked to original sources

bioRxiv2026

The development of slow and fast theta oscillations in the human brain

Theta oscillations are pivotal for attention and memory, with developmental shifts in theta activity and connectivity tracking memory ability across the lifespan. Building on initial evidence that the theta band separates into slow and fast sub-bands across development within the medial temporal lobe (MTL) and the prefrontal cortex (PFC), this preregistered study (https://osf.io/gsru7) characterized the frequencies of slow (~1.5-4.5 Hz) and fast (~4.5-9.0 Hz) theta oscillations across the brain in a large developmental cohort. We analyzed intracranial EEG (iEEG) recordings from widespread brain regions in 101 children and adults (5.93-54.00 years, 63 males; n electrodes = 5691) using task-based (attention to to-be-remembered visual stimuli) and task-free (resting-state) data. We reveal distinct slow and fast theta oscillations in all regions and identify a spatial gradient in both frequency ranges, with slow theta oscillations speeding up and fast theta oscillations slowing down from posterior to anterior regions. The dissociation between slow and fast theta frequencies peaks during young adulthood, mirroring the developmental trajectory of attention and memory. In the hippocampus and PFC, attentional state (task-based, task-free) modulated age effects, and individual differences in task-based slow theta frequencies predicted individual differences in memory performance, linking the development of slow theta oscillations to the development of memory. Gray matter volume was not associated with age- or task-related differences in theta frequencies, suggesting that the development of theta oscillations is independent of regional brain structure. This study establishes developmental trajectories of slow and fast theta oscillations in localized brain regions and links the maturation of hippocampal and PFC slow theta oscillations to the development of attention and memory.

neuroscience↗

bioRxiv2026

ProtSpace: Protein Universe in Your Browser

AO_SCPLOWBSTRACTC_SCPLOWProtein Language Models (pLMs) generate per-protein embeddings that encode functional, structural, and evolutionary information, yet the relationships captured in these representations remain difficult to explore systematically. ProtSpace (https://protspace.app) is a web application for interactive visualization of pLM embedding spaces, enabling hypothesis generation directly in the browser without installation. Unlike traditional network-based tools that exclusively visualize amino acid sequence similarity, ProtSpace explores embedding spaces, revealing relationships often not captured by traditional comparisons. Users provide protein sequences or pre-computed embeddings through a Google Colab notebook or the Python CLI; the pipeline applies dimensionality reduction, retrieves 38 annotation types spanning UniProt, InterPro, NCBI Taxonomy, TED structural domains, and sequence-based predictors served via Biocentral, and produces a portable binary file for the browser-based viewer. WebGL-accelerated rendering supports interactive exploration of over 570,000 proteins. Distinctive features include per-point pie charts for multi-label annotations and integrated 3D structure viewing through AlphaFold2 predictions. All computation happens on the users machine, ensuring data privacy. We demonstrate the utility of ProtSpace through a progressive zoom-in across biological scales: from global proteome organization of Swiss-Prot, through cross-species comparison revealing conserved and lineage-specific families, to functional hypothesis generation within the beta-lactamase superfamily. ProtSpace is freely available at https://protspace.app under the Apache 2.0 license. KO_SCPLOWEYC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWPOINTSC_SCPLOWO_LIProtSpace is a free, open-source web application that visualizes protein Language Model (pLM) embeddings as interactive maps, scaling to 570,000 proteins entirely client-side. C_LIO_LIA zero-installation Google Colab notebook and a Python CLI prepare visualization-ready bundles from FASTA files, UniProt queries, or pre-computed HDF5 embeddings, automatically retrieving 38 annotation types from five sources (UniProt, InterPro, NCBI Taxonomy, TED structural domains, and Biocentral sequence predictors) alongside custom CSV metadata. C_LIO_LIApplication examples demonstrate that embedding visualizations generate testable biological hypotheses at multiple scales, from proteome-wide organization through species-level comparison to family-level functional discovery, and that these are complementary to traditional sequence-based analyses. C_LI

bioinformatics↗

bioRxiv2025

Declarative Memory Through the Lens of Single-Trial Peaks in High-Frequency Power

Declarative memory depends on the coordination of local processing, indexed by high-frequency broadband (HFB) activity, with global network organization, indexed by theta oscillations. However, theta and HFB exhibit asynchronous timing, raising the question of how results of local processing are communicated throughout the network. Using intracranial EEG in patients performing a recognition memory task, we examined this coordination across the medial temporal lobe (MTL) and prefrontal cortex (PFC). HFB peak activity was earlier in the MTL than PFC. Anchoring analyses of theta phase clustering and connectivity to HFB peaks revealed strong phase clustering locked to HFB peaks in the PFC, as well as connectivity between the PFC and MTL that predicted individual memory performance. Graph analysis revealed specific connections amidst sparse network connectivity during memory success. This study demonstrates that transient brain states linked to internal physiological events support memory and refines our understanding of local and network-level process interactions. HighlightsO_LIMemory-linked theta activity is time-locked to internal brain events C_LIO_LINetwork connectivity changes dynamically during memory processing C_LIO_LISparse network connectivity supports successful memory C_LIO_LISpecific sequences of transient states may be critical for declarative memory C_LI

neuroscience↗

bioRxiv2024

The development of aperiodic neural activity in the human brain

The neurophysiological mechanisms supporting brain maturation are fundamental to attention and memory capacity across the lifespan. Human brain regions develop at different rates, with many regions developing into the third and fourth decades of life. Here, in this preregistered study (https://osf.io/gsru7), we analyzed intracranial EEG (iEEG) recordings from widespread brain regions in a large developmental cohort. Using task-based (i.e., attention to-be-remembered visual stimuli) and task-free (resting-state) data from 101 children and adults (5.93 - 54.00 years, 63 males; n electrodes = 5691), we mapped aperiodic (1/[f]-like) activity, a proxy of neural noise, with steeper slopes indexing less noise and flatter slopes indexing more noise. We reveal that aperiodic slopes flatten with age into young adulthood in both association and sensorimotor cortices, challenging models of early sensorimotor development based on brain structure. In prefrontal cortex (PFC), attentional state modulated age effects, revealing steeper task-based than task-free slopes in adults and the opposite in children, consistent with the development of cognitive control. Age-related differences in task-based slopes also explained age-related gains in memory performance, linking the development of PFC cognitive control to the development of memory. Last, with additional structural imaging measures, we reveal that age-related differences in gray matter volume are similarly associated with aperiodic slopes in association and sensorimotor cortices. Our findings establish developmental trajectories of aperiodic activity in localized brain regions and illuminate the development of PFC control during adolescence in the development of attention and memory.

neuroscience↗

bioRxiv2023

Modeling and dissociation of intrinsic and input-driven neural population dynamics underlying behavior

Neural dynamics can reflect intrinsic dynamics or dynamic inputs, such as sensory inputs or inputs from other regions. To avoid misinterpreting temporally-structured inputs as intrinsic dynamics, dynamical models of neural activity should account for measured inputs. However, incorporating measured inputs remains elusive in joint dynamical modeling of neural-behavioral data, which is important for studying neural computations of a specific behavior. We first show how training dynamical models of neural activity while considering behavior but not input, or input but not behavior may lead to misinterpretations. We then develop a novel analytical learning method that simultaneously accounts for neural activity, behavior, and measured inputs. The method provides the new capability to prioritize the learning of intrinsic behaviorally relevant neural dynamics and dissociate them from both other intrinsic dynamics and measured input dynamics. In data from a simulated brain with fixed intrinsic dynamics that performs different tasks, the method correctly finds the same intrinsic dynamics regardless of task while other methods can be influenced by the change in task. In neural datasets from three subjects performing two different motor tasks with task instruction sensory inputs, the method reveals low-dimensional intrinsic neural dynamics that are missed by other methods and are more predictive of behavior and/or neural activity. The method also uniquely finds that the intrinsic behaviorally relevant neural dynamics are largely similar across the three subjects and two tasks whereas the overall neural dynamics are not. These input-driven dynamical models of neural-behavioral data can uncover intrinsic dynamics that may otherwise be missed.

neuroscience↗